Long-term trends of inequalities in mortality in 6 European countries

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O R I G I N A L A R T I C L E

Long-term trends of inequalities in mortality in 6 European countries

Rianne de Gelder.Gwenn Menvielle .Giuseppe Costa. Katalin Kova´cs.Pekka Martikainen.Bjørn Heine Strand. Johan P. Mackenbach

Received: 29 April 2016 / Revised: 1 November 2016 / Accepted: 10 November 2016 ÓThe Author(s) 2016. This article is published with open access at Springerlink.com

Abstract

Objectives We aimed to assess whether trends in inequalities in mortality during the period 1970–2010 dif- fered between Finland, Norway, England and Wales, France, Italy (Turin) and Hungary.

Methods Total and cause-specific mortality data by educational level and, if available, occupational class were collected and harmonized. Both relative and absolute measures of inequality in mortality were calculated.

Results In all countries except Hungary, all-cause mortality declined strongly over time in all socioeconomic

groups. Relative inequalities in all-cause mortality gener- ally increased, but more so in Hungary and Norway than elsewhere. Absolute inequalities often narrowed, but went up in Hungary and Norway. As a result of these trends, Hungary (where inequalities in mortality where almost absent in the 1970s) and Norway (where inequalities in the 1970s were among the smallest of the six countries in this study) now have larger inequalities in mortality than the other four countries.

Conclusions While some countries have experienced dra- matic setbacks, others have made substantial progress in reducing inequalities in mortality.

Keywords MortalitySocioeconomic inequalities TrendsEurope

Introduction

Widening relative and/or absolute inequalities in mortality over the past two decades have been reported from many countries (Borrell et al. 2008; Fawcett et al.2005; Jemal et al. 2008; Krieger et al. 2008; Mackenbach et al.2003;

Mackenbach et al. 2014; Martikainen et al. 2014; Tarki- ainen et al.2012; Strand et al.2010,2014), but studies of long-term trends stretching over three or more decades are rare, and are usually limited to a single country. Examples include studies of long-term trends in the United States (Krieger et al. 2008), Finland (Martikainen et al. 2014), Norway (Strand et al. 2014), France (Menvielle et al.

2007), England and Wales (Capewell and Graham 2010) and Italy (Stringhini et al.2015).

While short-term trends are important for monitoring, for example because they reflect the effect of changes in exposure to determinants of mortality with a relatively Electronic supplementary material The online version of this

article (doi:10.1007/s00038-016-0922-9) contains supplementary material, which is available to authorized users.

R. de GelderJ. P. Mackenbach (&)

Department of Public Health, Erasmus MC, Rotterdam, The Netherlands

e-mail: [email protected] G. Menvielle

UPMC Univ Paris 06, INSERM, Institut Pierre Louis d’Epide´miologie et de Sante´ Publique (IPLESP UMRS 1136), Sorbonne Universite´s, Paris, France

G. Costa

Department of Clinical Medicine and Biology, University of Turin, Turin, Italy

K. Kova´cs

Demographic Research Institute, Budapest, Hungary P. Martikainen

Department of Sociology, University of Helsinki, Helsinki, Finland

B. H. Strand

Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway

DOI 10.1007/s00038-016-0922-9

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immediate impact, such as improvements in medical treatment or road safety, long-term trends may provide insights into how secular changes in mortality and its determinants play out in the evolving pattern of health inequalities. For example, high-income countries are in an advanced stage of the epidemiologic transition (Olshansky and Ault 1986; Omran 1971), with rapidly but differen- tially declining rates of cardiovascular disease mortality and widening inequalities in cardiovascular disease mortality as a result (Avendano et al.2006; Kunst et al.1999;

Marmot and McDowall 1986). Such secular changes can only be captured on a time-scale of three or four decades.

Previous studies have provided a mixed picture of long- term trends in inequalities in mortality. Relative inequalities (e.g., inequalities expressed in terms of a rate ratio, indicating the strength of the association between socioeconomic position and mortality regardless of the absolute level of mortality) seem to have universally widened, even in the highly developed welfare states of Western Europe, but for absolute inequalities (e.g., inequalities expressed in terms of a rate difference, indicating the absolute mortality excess in lower as compared to higher socioeconomic groups) both widening and narrowing have been reported (Martikainen et al. 2014; Strand et al. 2014; Shkolnikov et al.2012; Stringhini et al.2015; Menvielle et al. 2007).

Because almost no studies have quantitatively compared these trends between countries, it is unknown whether countries differ in the timing of widening or narrowing of inequalities in mortality.

We therefore analyzed trends in socioeconomic inequalities in mortality over four decades, using a unique dataset with harmonized data from six European countries.

In addition to the Western European countries mentioned above, this dataset also covers Hungary, thereby providing a first analysis of long-term trends in inequalities in mortality in Central/Eastern Europe—a part of the subcontinent whose political history had a profound impact on mortality and life expectancy (Mackenbach2013).

Methods

Data

In this study mortality data were used from Finland, Norway, England and Wales, France, Italy and Hungary, based on a total of 269,550,158 person years, covering the period 1970–2010. Key characteristics of the data are shown in Table1. Data were harmonized to enhance between- and within-country comparability and contained information on sex, age, educational level, occupational class, and cause- specific mortality. The educational distribution of each country’s population is presented in web appendix Table 1.

Most data sets covered the entire national territories, but in Italy data from the Turin region only were available. Previous studies have shown that patterns and trends observed in this regional population correspond well to those seen at the national level (Federico et al.2013; Marinacci et al.2013).

Data from Finland, Norway, England and Wales, France and Italy (Turin) were generated in a longitudinal mortality follow-up after a census, in which mortality data were linked to socioeconomic information that had been recorded in the census; each decade of follow-up was divided in approxi- mate 5-year periods for the analysis to allow a more fine- grained analysis of time-trends. Hungary has so-called cross- sectional unlinked data in which socioeconomic information on the population-at-risk comes from the census, and on the deceased comes from the death certificate.

We used two indicators of socioeconomic position: level of education and occupational class. Education was classified according to the International Standard Classification of Education 1997 (UNESCO 2006). Three groups were distinguished: ‘primary education and lower secondary education (ISCED 0, 1 and 2; ‘low’)’, ‘upper secondary education and post-secondary, non-tertiary education (ISCED 3 and 4; ‘middle’)’, and tertiary education (ISCED 5 and 6; ‘high’). In the datasets for 1981–1991 and 1991–2001 in England and Wales only two levels of education could be distinguished (‘low and middle’ vs ‘high’), and we therefore focussed on the periods 1971–1981 and 2001–2009 for the main analysis. Occupational class was classified following the Erikson–Goldthorpe–Portocarero (EGP) social class scheme (Erikson and Goldthorpe1992).

Four categories were distinguished: non-manual workers, manual workers, farmers and self-employed, but because the relative position of farmers and self-employed workers in the social hierarchy is ambiguous, and information on occupational class is less reliable for women, we only report inequalities between men in manual and non-manual occupations. Because data on education were available for all countries, whereas data on occupation were available for men in 4 countries only, inequalities by educational group are presented as main outcome.

Deaths were classified according to the 8th, 9th or 10th revision of the International Classification of Diseases (ICD). ICD-codes were harmonized following the scheme presented in web appendix Table 2. Our main outcome variables were all-cause mortality, and mortality due to cardiovascular disease (ICD-10 I00-I99), cancer (ICD-10 C00-D48), all other diseases (ICD-10 A00-B99, D5-H95 and J00-U85), and external causes (i.e., injuries; V01-Y98).

Analysis

Age-standardized mortality rates by educational level, sex, country and time-period were calculated using the

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European Standard Population (Ahmad et al. 2001).

Analyses were restricted to persons aged 35–79 for analyses by educational level (40–79 in Norway), and to persons aged 35–64 (active working population) for analyses by occupational class. Ages refer to age at death.

Educational inequalities in mortality were assessed with both relative and absolute measures, using the Relative Index of Inequality (RII) and the Slope Index of Inequality (SII) (Mackenbach and Kunst 1997; Mackenbach et al.

2008). The RII and SII are regression-based measures which take into account the distribution of education in a population, and adjust the relative position of each group to its share in the population, which increases comparability over time and between countries if there are substantial changes or differences in distribution of the population over socioeconomic groups. RIIs were calculated with Poisson regression with educational ‘rank’ as an indepen- dent variable, controlling for age (in 5-year age groups).

Educational ‘rank’ was calculated for each education group (by country, sex and period) as the mean proportion of the population having a higher level of education. This ensures that all education groups (not only the lowest and highest) are taken into account, and that the magnitude of inequalities in mortality can be compared between countries even if their educational distributions are different.

The RII is a relative measure which can be interpreted as the rate ratio of mortality among those with the very lowest educational level compared to those with the very highest educational level. SIIs were calculated from the RIIs and the age-standardized mortality rates (ASMR) in the general population using the formula: SII=2 9ASMR9(- RII-1)/(RII?1). The SII is an absolute measure which can be interpreted as the rate difference of mortality between those with the very lowest and those with the very highest educational level (Mackenbach and Kunst 1997;

Mackenbach et al. 2008). We calculated 95% CIs using Table 1 Key characteristics of the datasets used in the analysis

Country Type Years Census date Geographic

coverage

Ages included

Number of deaths

Number of person years Finland Longitudinal Dec 31, 1970 to Dec 31, 1980 31.12.1970 National 35–79 310,253 19,401,076

Dec 31, 1980 to Dec 31, 1990 31.12.1980 297,925 22,132,564

Dec 31, 1990 to Dec 31, 2000 31.12.1990 271,246 25,148,551

Dec 31, 2000 to Dec 31, 2010 31.12.2000 247,441 27,446,463

Norway Longitudinal Nov, 1970 to Dec, 1980 Nov, 1970 National 40–79 237,790 14,097,453

Nov, 1980 to Dec, 1990 Nov, 1980 235,645 13,804,407

Nov, 1990 to Dec, 2001 Nov, 1990 227,996 16,372,733

Nov, 2001 to Dec, 2009 Nov, 2001 133,675 14,050,440

England/Wales Longitudinal Apr 25, 1971 to Apr 4,1981 25.4. 1971 National 35–79 40,543 2,315,725

Apr 5, 1981 to Apr 20,1991 5.4.1981 36,715 2,453,660

Apr 21, 1991 to Apr 28, 2001 21.4.1991 31,472 2,579,226

Apr 29, 2001 to Dec 31, 2009 29.4. 2001 22,009 2,431,057

France Longitudinal 10.1.1975 to 9.1.1982 10.1.1975 National 35–79 16,940 1,397,501

10.1.1982 to 9.1.1990 10.1.1982 19,236 1,689,497

10.1.1990 to 9.1.1999 10.1.1990 19,331 2,113,937

10.1.1999 to 31.12.2007 10.1.1999 15,663 1,745,199

Italy (Turin) Longitudinal Oct 24, 1971 to Oct 24, 1981 24.10.1971 City 35–79 58,426 4,689,534

Oct 25, 1981 to Oct 19, 1991 25.10.1981 56,947 5,118,272

Oct 20, 1991 to Oct 20, 2001 20.10.1991 46,181 4,736,315

Oct 21, 2001 to Dec 31, 2010 21.10.2001 35,056 4,283,032

Hungary CS, unlinked 1971–1974 1.1.1973 National 35–79 340,511 19,754,780

1978–1981 1.1.1980 393,590 20,180,700

1988–1991 1990 385,974 20,576,688

1999–2002 2001 369,773 21,031,348

Data from England and Wales and France concern a 1% representative sample of the total population In France, only those born in mainland France were included (excluding overseas territories and abroad) CScross-sectional

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bootstrapping of 1000 replicas. As our analysis of mortality by occupational class only involved two classes (manual and non-manual), we used simple Rate Ratios and Rate Differences with the non-manual class as the reference group for this socioeconomic indicator.

Results

In this study, a total of 3,850,338 deaths were observed.

Tables2 and 3 present all-cause mortality rates by education for each country and time-period, and changes in mortality rates over time. Full details on mortality rates by cause of death, sex, time-period and education in each country are given in web appendix Table 3. In the early 1970s, all-cause mortality was already highest among the lowest educated in all countries among both men and women, with the exception of Hungarian women for whom mortality was slightly higher among the high educated, and even 1.4 times higher among the higher educated in the early 1980s. Since the early 1980s, mortality gradually decreased over time in all educational groups, except in Hungary where mortality among the low and middle educated increased until the early 1980s (women) and early 1990s (men) and then also started to decline.

Relative (i.e., percentage) declines were almost always largest among the high educated. As a result, relative inequalities in all-cause mortality, as measured by the RII, went up in most countries (Fig.1). The increase was strongest in Hungary, where the RII went up from a level that was among the lowest among men, and even slightly below 1.00 among women, in the early 1970s to a level that was higher than that in any other country in the early 2000s. Norway also stands out as a country with a relatively steep increase of the RII, both among men and women. In France (men and women) and Italy (women), on the other hand, RIIs did not change significantly over time, as indicated by the overlapping 95% confidence intervals of the first and last observation periods.’’

Trends in absolute inequalities in all-cause mortality by education were more variable (Fig.2). This is due to the fact that absolute declines in mortality were largest among the low educated in some countries [England and Wales, France and Italy (Turin)], but not in others (Norway and Hungary) (Tables2,3). Over time, absolute inequalities in mortality as measured by the SII went down among men in most countries, but not in Norway where the SII increased until the late 1990s and only then started to decline (Fig.2a). In Hungary, due to the enormous rise of mortality among low educated men, the SII for all-cause mortality increased from a relatively low level in the early 1970s to a very high level in the early 2000s.

Among women, although differences in absolute mortality declines between the low and high educated were usually smaller than among men (Tables2, 3), absolute inequalities in all-cause mortality among women increased in Hungary and Norway, and decreased in Finland, Eng- land and Wales and Italy (Turin), as they did in men (Fig.2b).

Figure2 also shows that the role of cardiovascular diseases in generating inequalities in all-cause mortality has changed considerably over time, particularly in Finland, England and Wales, and Norway. In these countries, cardiovascular diseases used to be the main contributor to inequalities in all-cause mortality among both men and women, but in the most recent periods this was no longer the case. For example, among Finnish women the contribution of cardiovascular diseases to inequality in all-cause mortality (calculated as 1009(SII for cardiovascular diseases mortality)/(SII for all-cause mortality)) decreased from 72% in the early 1970s to 36% in the late 2000s. In most countries, absolute decreases in cardiovascular mortality rates were largest among the lower educated (web appendix Table 3), and as a result, absolute inequalities in cardiovascular mortality declined considerably (Fig.2), although relative inequalities went up (web appendix figure 1).

The declining contribution of cardiovascular diseases to inequality in all-cause mortality implies an increasing contribution of other causes of death: in three out of six countries the contribution of cancer mortality to inequality in all-cause mortality increased among both men and women. The continued increase of absolute inequalities in Norway until the late 1990s was due to a rise of inequalities in cancer and other diseases among both men and women, which more than compensated for the decline of inequalities in cardiovascular disease (Fig.2). Furthermore, the massive rise of inequalities in all-cause mortality in Hun- gary was due to rises seen for several causes of death, and to a reversal of formerly ‘negative’ inequalities in cancer mortality (indicating higher mortality among the high educated) to ‘positive’ inequalities in the last observation period.

All-cause mortality trends by occupational class were partly similar to trends by education. In the four countries for which data were available (Finland, England and Wales, France and Italy—men only), mortality rates declined among both manual and non-manual workers, with relative declines usually being largest among non- manual workers, and differences in absolute declines being more variable (web appendix Table 4). This resulted in increasing relative inequalities by occupational class in most countries, as we observed for inequalities by education; however, absolute inequalities decreased in France only, and were stable in the other three countries (Fig.3).

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Table2Age-standardizedall-causemortalityratesper100.000personyears,byeducationalgroup,menandwomen Country/periodEducationallevel TotalLowMiddleHigh ASMR95%CIASMR95%CIASMR95%CIASMR95%CI Finland 1970–19742205.3(2191.6–2219.9)2307.7(2291.6–2324.1)1769.5(1720.6–1815.8)1669.2(1621.7–1716.2) 1975–19792014.2(2000.9–2028.2)2128.9(2113.8–2143.8)1598.9(1556.1–1638.1)1498.2(1461.5–1536.6) 1980–19841812.5(1800.7–1823.5)1934.9(1921.6–1949.2)1575.4(1537.1–1611.1)1290.6(1260.4–1324.3) 1985–19891673.1(1660.9–1683.3)1820.2(1806.8–1834.2)1485.1(1453.9–1515.6)1156.7(1131.8–1183.7) 1990–19941485.2(1475.3–1495.8)1655.3(1642.5–1668.8)1344.4(1318.4–1371.7)997.7(975.5–1020.9) 1995–19991304.9(1296.2–1314.6)1499.0(1484.3–1511.6)1213.6(1192.4–1236.7)842.8(824.2–861.0) 2000–20041130.6(1123.1–1139.2)1363.2(1349.7–1375.9)1063.8(1047.1–1080.6)718.3(703.8–733.1) 2005–20091016.6(1009.8–1024.2)1294.8(1280.2–1309.4)1003.5(990.4–1018.2)627.6(615.8–639.0) Percentchangelast—first(95%CI)-53.9%(-54.3to-53.5%)-43.9%(-44.6to-43.2%)-43.3%(-44.9to-41.6%)-62.4%(-63.6to-61.1%) Absolutechangelast—first(95%CI)-1188.7(-1204.6to-1172.8)-1012.9(-1034.7to-991.1)-766.0(-815.6to-716.4)-1041.6(-1090.3to-992.9) Norway 1970–19741727.1(1716.0–1738.0)1827.9(1810.0–1845.5)1595.6(1578.1–1615.6)1329.7(1287.5–1372.2) 1975–19791711.9(1700.8–1723.6)1838.6(1818.5–1860.4)1599.6(1574.9–1616.0)1255.0(1216.2–1293.0) 1980–19841628.1(1617.6–1638.8)1780.5(1764.6–1801.0)1514.2(1494.6–1530.2)1180.7(1149.7–1211.5) 1985–19891594.0(1581.9–1603.9)1792.1(1768.9–1812.8)1470.3(1449.8–1487.7)1085.0(1053.5–1115.3) 1990–19941412.3(1402.0–1422.0)1656.4(1638.5–1675.8)1310.6(1293.4–1323.4)945.7(917.0–967.1) 1995–19991245.4(1236.6–1254.7)1572.3(1554.2–1596.1)1146.9(1133.5–1162.7)800.8(782.5–818.8) 2000–20041028.2(1020.2–1035.3)1381.2(1361.1–1405.7)953.4(940.5–965.9)660.3(642.4–677.8) 2005–2009933.8(922.9–943.8)1284.2(1253.5–1310.3)892.4(880.0–906.2)596.5(578.5–614.0) Percentchangelast—first(95%CI)-45.9%(-46.7to-45.2%)-29.7%(-31.3to-28.2%)-11.1%(-45.4to-42.8%)-55.1%(-57.3to52.9%) Absolutechangelast—first(95%CI)-793.3(-810.1to-776.5)-543.7(-574.1to-513.3)-703.2(-732.0to-674.4)-733.2(-781.9to-684.5) EnglandandWales 1970–19741969.3(1935.0–2002.1)1965.1(1928.3–2005.4)NANA1493.0(1333.2–1655.0) 1975–19791868.1(1836.0–1902.0)1898.5(1860.6–1935.9)NANA1220.7(1106.9–1332.8) 1980–19841650.6(1622.5–1682.0)1703.0(1665.3–1735.6)NANA1063.8(971.5–1154.9) 1985–19891486.7(1456.2–1516.7)1544.9(1515.3–1576.5)NANA976.2(895.2–1054.0) 1990–19941334.1(1305.5–1360.3)1389.4(1357.3–1421.4)NANA895.6(833.2–959.7) 1995–19991150.0(1125.6–1172.6)1215.1(1184.9–1245.8)NANA755.0(708.6–806.1) 2000–2004994.1(968.9–1014.2)1070.4(1043.1–1097.9)NANA677.0(629.4–722.6) 2005–2009809.9(785.5–833.1)876.7(846.6–906.7)NANA559.2(514.6–606.8)

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Table2continued Country/periodEducationallevel TotalLowMiddleHigh ASMR95%CIASMR95%CIASMR95%CIASMR95%CI Percentchangelast—first(95%CI)-58.9%(-60.3to-57.5%)-55.4%(-57.1to-53.8%)NANA-62.5%(-67.3to-57.2%) Absolutechangelast—first(95%CI)-1159.4(-1200.5to-1118.3)-1088.4(-1137.3to-1039.5)NANA-933.8(-1101.2to-766.4) France 1975–19791461.9(1428.5–1495.3)1565.1(1525.7–1601.8)1155.1(1061.9–1261.0)861.9(749.5–982.4) 1980–19841466.4(1424.1–1508.8)1588.4(1536.3–1642.8)1171.7(1067.8–1279.0)903.9(769.4–1046.6) 1985–19891423.7(1386.9–1460.4)1554.5(1508.2–1602.6)1181.9(1095.9–1269.2)880.0(772.1–989.1) 1990–19941199.5(1171.1–1227.9)1351.7(1315.6–1391.5)1030.8(972.2–1084.8)674.4(599.1–749.5) 1995–19991160.8(1130.4–1191.1)1356.5(1312.8–1404.1)986.7(934.8–1404.1)630.0(556.7–706.8) 2000–2004996.6(971.9–1021.4)1196.6(1155.8–1235.8)899.8(854.3–940.2)548.5(491.8–609.8) 2005–2009933.4(906.9–959.9)1150.3(1101.8–1196.7)865.3(822.1–907.5)576.5(520.8–633.2) Percentchangelast—first(95%CI)-36.2%(-38.5to-33.8%)-26.5%(-30.1to-23.1%)-25.1%(-31.7to-18.0%)-33.1%(-43.2to-19.9%) Absolutechangelast—first(95%CI)-528.5(-571.1to-485.9)-414.8(-475.6to-354.0)-289.8(-398.1to-181.5)-285.4(-414.7to-156.1) Italy(Turin) 1970–19741694.5(1669.0–1718.6)1729.6(1703.2–1758.0)1591.0(1501.0-1678.2)1321.8(1229.2–1414.7) 1975–19791607.5(1584.1–1632.2)1649.9(1624.6–1676.9)1437.0(1351.8–1513.5)1289.8(1200.9–1385.5) 1980–19841429.4(1407.2–1451.0)1482.2(1458.1–1505.6)1259.1(1196.3–1328.9)1034.9(962.5–1098.7) 1985–19891251.0(1232.2–1270.6)1309.9(1286.9–1332.7)1096.0(1039.3–1148.5)910.9(848.5–975.7) 1990–19941120.3(1102.2–1138.3)1192.7(1171.7–1214.4)978.6(933.2–1025.6)808.9(758.4–860.3) 1995–1999941.2(924.8–957.3)1019.9(999.0–1041.9)792.0(754.0–830.5)669.1(623.3–712.9) 2000–2004807.5(793.4–821.6)904.5(884.3–924.6)668.6(638.5–698.7)571.8(533.8–609.1) 2005–2009715.2(699.7–731.6)826.1(803.8–850.0)596.4(565.0–625.6)485.7(448.9–524.4) Percentchangelast—first(95%CI)-57.8%(-59.0to-56.6%)-52.2%(-53.7to-50.7%)-62.5%(-65.1to-59.6%)-63.3%(-66.9to-59.1%) Absolutechangelast—first(95%CI)-979.3(-1008.8to-949.8)-903.5(-939.3to-867.7)-994.6(-1088.2to-901.0)-836.1(-936.2to-736.0) Hungary 1970–19741997.4(1989.3–2004.7)2033.4(2023.9–2044.4)1748.1(1714.1–1782.5)1769.3(1730.3–1810.9) 1980–19842296.8(2287.8–2305.2)2373.6(2363.9–2386.3)1970.4(1944.4–2005.3)1998.4(1966.1–2029.9) 1990–19942436.0(2426.5–2445.2)2650.9(2640.2–2662.6)2103.9(2074.4–2134.5)1409.4(1385.2–1433.8) 2000–20042194.5(2183.9–2202.3)2623.7(2614.2–2638.5)1469.5(1449.4–1484.2)1020.4(1003.3–1033.3) Percentchangelast—first(95%CI)?9.9%(9.2–10.5%)?29.0%(28.2–29.9%)-15.9%(-17.8to-13.9%)-42.3%(-43.9to-40.8%) Absolutechangelast—first(95%CI)?197.1(185.1–209.1)?590.4(574.5–606.2)-278.6(-316.9to-240.2)-749.0(-792.0to-706.0) InEnglandandWales,mortalityratesamong‘low’and‘middle’educatedwerecombinedbecause‘middle’educationisnotavailableinthe1980sand1990s ASMRage-standardizedmortalityrate,NAnotavailable,CIconfidenceinterval

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Table3Age-standardizedall-causemortalityratesper100.000personyears,byeducationalgroup,women Country/periodEducationallevel TotalLowMiddleHigh ASMR95%CIASMR95%CIASMR95%CIASMR95%CI Finland 1970–19741041.7(1033.8–1049.3)1076.9(1068.4–1085.2)809.7(781.6–836.7)794.5(763.0–824.6) 1975–1979881.8(874.6–888.7)916.2(907.4–923.9)714.1(691.8–736.0)673.3(647.3–700.0) 1980–1984794.8(788.4–801.2)830.1(822.7–837.2)674.4(655.3–691.8)591.9(570.7–613.4) 1985–1989758.2(751.8–764.3)800.0(792.0–807.6)666.8(651.3–682.6)568.5(549.6–587.7) 1990–1994679.2(673.3–684.7)739.8(732.5–747.1)584.2(571.1–597.1)508.4(492.1–525.1) 1995–1999595.3(590.0–600.2)669.8(661.6–677.9)524.4(513.7–535.7)434.3(421.0–447.7) 2000–2004527.9(523.1–532.7)641.3(632.4–649.7)470.0(461.2–479.8)374.3(363.2–384.4) 2005–2009469.7(464.6–474.1)613.1(602.0–623.4)437.3(429.3–445.4)336.1(327.2–344.4) Percentchangelast—first(95%CI)-54.9%(-55.4to-54.3%)-43.1%(-44.2to-42.0%)-46.0%(-48.2to-43.9%)-57.7%(-59.6to-55.7%) Absolutechangelast—first(95%CI)-572.0(-581.1to-562.9)-463.8(-477.4to-450.2)-372.4(-401.1to-343.7)-458.4(-490.4to-426.4) Norway 1970–1974938.3(929.5–945.8)1000.7(992.4–1011.2)774.5(758.1–786.5)689.2(658.5–729.9) 1975–1979874.5(865.1–884.4)935.8(927.6–947.8)753.4(737.6–768.3)653.6(623.0–681.1) 1980–1984799.3(792.0–806.4)857.5(848.7–866.3)700.2(687.5–711.0)597.1(574.7–623.2) 1985–1989802.3(795.2–810.1)872.8(862.6–885.6)700.5(689.2–712.5)586.4(561.1–620.4) 1990–1994737.5(730.9–744.1)835.6(826.2–846.7)645.8(635.1–656.3)507.5(486.4–522.8) 1995–1999693.0(686.7–700.1)849.3(837.5–861.5)602.5(591.6–611.4)447.5(432.2–462.7) 2000–2004612.8(606.1–619.2)789.8(778.1–800.6)537.9(528.8–547.3)403.6(391.5–418.4) 2005–2009572.1(564.4–579.7)764.3(749.0–781.6)513.5(500.4–523.2)372.3(360.9–388.6) Percentchangelast—first(95%CI)-39.0%(-40.0to-38.1%)-23.6%(-25.3to-21.6%)-33.7%(-35.7to-31.6%)–46.0%(-49.6to-42.1%) Absolutechangelast—first(95%CI)-366.2(-377.9to-354.5)-236.4(-257.4to-215.4)-261.0(-280.8to-241.2)–316.9(-357.4to-276.4) EnglandandWales 1970–19741114.9(1091.7–1138.8)1090.8(1066.0–1114.2)NANA828.5(724.5–948.8) 1975–19791001.1(979.8–1021.8)1009.4(986.0–1034.4)NANA686.2(602.0–776.2) 1980–1984929.9(909.3–951.4)942.6(922.4–964.3)NANA677.8(596.4–760.2) 1985–1989842.7(822.5–863.5)857.0(837.3–877.6)NANA630.9(559.9–701.5) 1990–1994795.6(776.3–815.3)810.9(789.2–832.5)NANA557.2(505.0–617.4) 1995–1999721.2(702.9–740.9)747.0(725.6–769.3)NANA459.2(415.7–505.3) 2000–2004639.7(623.5–656.7)673.9(655.0–693.7)NANA443.4(403.0–484.4) 2005–2009569.7(551.0–587.8)610.8(590.2–634.4)NANA417.5(378.2–459.5)

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Table3continued Country/periodEducationallevel TotalLowMiddleHigh ASMR95%CIASMR95%CIASMR95%CIASMR95%CI Percentchangelast—first(95%CI)-48.9%(-50.8to-46.7%)-44.0%(-46.4to-41.6%)NANA-49.6%(-57.0to-39.7%) Absolutechangelast—first(95%CI)-545.2(–575.1to-515.3)–480.0(–512.7to-447.3)NANA-411.0(-530.3to-291.7) France 1975–1979665.0(645.2–684.8)681.0(660.0–703.3)561.3(489.4–641.5)485.8(372.7–591.8) 1980–1984619.8(595.4–644.2)637.4(611.4–664.4)523.3(436.5–613.6)480.9(346.5–625.6) 1985–1989589.9(568.9–610.9)614.0(590.9–637.0)478.3(411.4–546.9)366.1(277.8–458.7) 1990–1994510.2(493.5–526.8)544.9(524.0–566.0)412.0(372.6–454.4)337.3(270.4–408.8) 1995–1999489.9(471.9–507.8)548.2(524.3–573.2)364.6(328.1–401.9)330.3(265.0–402.0) 2000–2004424.1(409.2–438.9)475.4(454.2–495.7)379.1(348.6–406.5)287.2(241.5–331.2) 2005–2009427.9(411.1–444.6)496.7(472.3–526.1)381.5(351.5–412.8)282.6(238.3–327.2) Percentchangelast—first(95%CI)-35.7%(-38.8to-32.4%)-27.1%(-31.5to-22.7%)-32.0%(-41.8to-20.4%)-41.8%(–55.5to-20.4%) Absolutechangelast—first(95%CI)-237.1(-263.0to-211.2)-184.3(-218.8to-149.8)-179.8(-261.8to-97.8)-203.2(-321.4to-85.0) Italy(Turin) 1970–1974866.2(851.2–881.5)877.6(862.0–893.5)715.7(654.7–776.4)615.1(499.0–741.1) 1975–1979809.4(794.2–824.3)819.7(804.5–835.0)687.1(634.4–749.2)593.2(499.2–706.1) 1980–1984718.7(706.8–731.4)724.5(711.6–738.3)620.2(575.4–665.6)597.6(508.8–689.3) 1985–1989620.9(609.5–633.5)629.4(616.0–642.8)543.1(501.9–583.1)506.0(428.7–582.1) 1990–1994556.7(545.7–568.1)574.0(559.4–587.1)463.2(431.8–495.4)432.6(378.0–488.7) 1995–1999495.3(484.2–506.6)507.9(495.0–520.2)447.1(417.4–477.5)415.4(372.5–462.0) 2000–2004422.9(412.6–433.2)438.1(425.8–450.7)406.9(382.1–431.0)380.6(347.0–416.4) 2005–2009367.8(357.1–377.6)401.7(386.7–416.4)316.5(295.8–339.8)293.2(260.3–327.0) Percentchangelast—first(95%CI)-57.5%(-58.8to-56.2%)-54.2%(-56.0to-52.4%)-55.8%(-60.7to-50.6%)–52.3%(-60.8to-39.7%) Absolutechangelast—first(95%CI)-498.4(-516.7to-480.1)-475.9(-497.5to-454.3)-399.2(-463.9to-334.5)–321.9(-447.5to-196.3) Hungary 1970–19741230.6(1224.2–1236.0)1228.5(1222.5–1234.8)1244.7(1214.8–1280.7)1320.7(1252.0–1387.9) 1980–19841276.5(1270.2–1282.4)1274.5(1268.8–1281.0)1286.9(1261.6–1311.4)1731.8(1671.0–1797.6) 1990–19941213.6(1207.8–1219.3)1248.9(1242.9–1257.8)1233.5(1212.2–1257.1)826.8(795.2–852.6) 2000–20041022.7(1018.1–1027.1)1145.8(1138.9–1153.0)718.3(708.4–730.6)706.1(683.0–722.3) Percentchangelast—first(95%CI)-16.9%(-27.5to-16.4%)-6.7%(-7.596to-6.0%)-42.3%(-44.0to-40.5%)-46.5%(-49.5to-43.4%) Absolutechangelast—first(95%CI)-207.9(-215.3to-200.4)-82.8(-92.1to-73.4)-526.4(-561.1to-491.6)-614.6(-685.4to-543.8) InEnglandandWales,mortalityratesamong‘low’and‘middle’educatedwerecombinedbecause‘middle’educationisnotavailableinthe1980sand1990s ASMRage-standardizedmortalityrate,NAnotavailable,CIconfidenceinterval

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0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Relave Index of Inequality (RII)

Period

3 5 4.0 4.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Relave Index of Inequality (RII)

Period

Finland Norway England & Wales France Italy (Turin) Hungary

a

b

Fig. 1 Trends in relative index of inequality for all-cause mortality by education for:

amen, andbwomen. In England and Wales, RIIs for the period 1980–1999 could not be calculated because ‘middle’

education was not available

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-500 0 500 1000 1500 2000 2500 3000

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Slope Index of Inequality (SII)

Period

England & Wales

-500 0 500 1000 1500 2000 2500 3000

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Period

Finland

-500 0 500 1000 1500 2000 2500 3000

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Period

Norway

2500 3000

y (SII)

France

₂₅₀₀

3000

y (SII)

Italy (Turin)

2500 3000

y (SII)

Hungary

-500 0 500 1000 1500 2000 2500

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Slope Index of Inequality (SII)

Period

France

-500 0 500 1000 1500 2000 2500

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Slope Index of Inequality (SII)

Period

Italy (Turin)

-500 0 500 1000 1500 2000 2500

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Slope Index of Inequality (SII)

Period

Hungary

all causes cardiovascular diseases cancer other diseases external causes

-250 0 250 500 750 1000

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Slope Index of Inequality (SII)

Period

England & Wales

-250 0 250 500 750 1000

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Slope Index of Inequality (SII)

Period

Finland

-250 0 250 500 750 1000

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Period

Norway

750 1000

ty (SII)

France

750 1000

y (SII)

Italy (Turin)

750 1000

y (SII)

Hungary

-250 0 250 500 750

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Slope Index of Inequality (SII)

Period

France

-250 0 250 500 750

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Period

Italy (Turin)

-250 0 250 500 750

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Slope Index of Inequality (SII)

Period

Hungary

all causes cardiovascular diseases cancer other diseases external causes

a

b

Fig. 2 Trends in slope index of inequality for all-cause and cause-specific mortality by education, by country:amen,bwomen. In England and Wales, SIIs for the period 1980–1999 could not be calculated because ‘middle’ education was not available